In medical ultrasound imaging, it is often desired to generate a boundary around an object of interest in an ultrasound image. For example, if the object of interest is a heart chamber, the area and perimeter of the chamber can be computed from the generated boundary. With this data, quantitative information can be obtained about various cardiac functions, such as cardiac wall motion, pressure-volume ratio, volumetric changes, blood pumping efficiency, and blood pumping volume. As another example, if the object of interest in the ultrasound image is a spectral Doppler strip, the boundary (corresponding to the envelope of the strip) represents the maximum velocity waveform--the highest velocity component in the Doppler spectrum that corresponds to flow.
To generate a boundary, a user employs either automatic or manual boundary-detection techniques. With automatic boundary-detection, various computational techniques can be used to automatically generate a boundary around an object of interest without user intervention. These techniques are well known in the art and generally detect the dividing line between a bright region and a dark region by thresholding ultrasound image data. While automatic boundary-detection techniques can quickly detect a boundary without user intervention, the detected boundary is often unacceptable. For example, a major problem in detecting boundaries in ultrasound images generated with fundamental frequencies is the presence of acoustic clutter. Acoustic clutter is generally associated with degraded beamforming due to body wall aberrations and is often present in obese patients, typically termed "hard-bodies." Acoustic clutter obscures the boundary between bright and dark regions, causing automatic boundary-detection algorithms to compute erroneous borders. Additionally, other forms of noise, such as speckle, can result in the generation of erroneous borders.
When erroneous borders are generated by an automatic boundary detection technique or when an automatic boundary detection technique is not used, a user typically must resort to manual tracing to generate a boundary. With manual boundary detection, the user views the ultrasound image on a display and traces an outline around the entire object of interest using a user interface, such as a track ball, joystick, or mouse. Because the ultrasound system uses the user-drawn boundary to calculate quantitative information, the user must be very precise in drawing the boundary. As a result, manual boundary detection is both a time consuming and taxing process for the user. This process is made even more difficult in ultrasound images generated with fundamental frequencies because acoustic clutter renders the borders difficult to see.
There is, therefore, a need for a method and system for boundary detection of an object of interest in an ultrasound image that will overcome the problems described above.